Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model
This work addresses the challenge of precise trajectory estimation for large, dynamic clusters in applications like biological behavior analysis, representing an incremental improvement over prior tracking methods.
The paper tackled the problem of 3D tracking of many similar, maneuvering objects from multiple views, such as in bio-cluster behavior studies, by proposing a current statistical model-based Kalman particle filter (CSKPF) method, which improved tracking integrity, continuity, and precision compared to existing methods in simulations and real experiments on fruitfly clusters.
Three-dimensional tracking of multiple objects from multiple views has a wide range of applications, especially in the study of bio-cluster behavior which requires precise trajectories of research objects. However, there are significant temporal-spatial association uncertainties when the objects are similar to each other, frequently maneuver, and cluster in large numbers. Aiming at such a multi-view multi-object 3D tracking scenario, a current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. The CSKPF algorithm predicts the objects' states and estimates the objects' state covariance by the current statistical model to importance particle sampling efficiency, and suppresses the measurement noise by the Kalman filter. The simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method. The real experiment on fruitfly clusters also confirms the effectiveness of the CSKPF method.